Chemistry teachers’ research competence following AI-supported professional development: a mixed-methods pre–post study

Introduction Developing teachers' research competence is a strategic priority in contemporary education, where artificial intelligence can support educational data analysis and evidence-informed pedagogical decision-making. This study assessed chemistry teachers' research competence, examined their reported practices and perceptions concerning students' social-emotional well-being, and investigated changes following participation in an AI-supported professional development program. Methods A quantitatively driven mixed-methods design with an embedded qualitative component was employed. Data were collected from 105 chemistry teachers using closed-ended questionnaire scales and two open-ended questions administered before and after a six-week professional development program. Descriptive statistics, Pearson's correlation analysis, paired-samples t-tests, Cohen's dz, and qualitative content analysis were used. Results Research competence was positively correlated with social-emotional monitoring practices ( r = .664, p < .01) and readiness to use AI ( r = .622, p < .01). Statistically significant pre-post improvements were observed across all measured dimensions ( p < .001), with Cohen's dz values ranging from 0.47 to 0.63. The qualitative findings indicated a shift in teachers' perceptions of AI from a tool used mainly for information retrieval and writing to one that could support research planning and data-informed decision-making. Discussion The findings indicate that AI-supported professional development can be associated with improvements in teachers' reported research competence, social-emotional monitoring practices, and readiness to use AI. The study proposes an empirically informed conceptual model integrating teacher inquiry, social-emotional monitoring, and potential AI-supported research practices.